{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Window Splitters in Sktime\n",
    "\n",
    "In this notebook we describe the window splitters included in the [`sktime.forecasting.model_selection`](https://github.com/alan-turing-institute/sktime/blob/main/sktime/forecasting/model_selection/_split.py) module. These splitters can be combined with `ForecastingGridSearchCV` for model selection (see [forecasting notebook](https://github.com/alan-turing-institute/sktime/blob/main/examples/01_forecasting.ipynb)). \n",
    "\n",
    "**Remark:** It is important to emphasize that for cross-validation in time series we can not randomly shuffle the data as we would be leaking information.\n",
    "\n",
    "**References:**\n",
    "- [Cross-validation: evaluating estimator performance](https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation)\n",
    "- [Cross-validation for time series](https://robjhyndman.com/hyndsight/tscv/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preliminaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from warnings import simplefilter\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "\n",
    "from sktime.datasets import load_airline\n",
    "from sktime.forecasting.base import ForecastingHorizon\n",
    "from sktime.forecasting.model_selection import (\n",
    "    CutoffSplitter,\n",
    "    ExpandingWindowSplitter,\n",
    "    SingleWindowSplitter,\n",
    "    SlidingWindowSplitter,\n",
    "    temporal_train_test_split,\n",
    ")\n",
    "from sktime.utils.plotting import plot_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_windows(y, train_windows, test_windows, title=\"\"):\n",
    "    \"\"\"Visualize training and test windows\"\"\"\n",
    "\n",
    "    simplefilter(\"ignore\", category=UserWarning)\n",
    "\n",
    "    def get_y(length, split):\n",
    "        # Create a constant vector based on the split for y-axis.\"\"\"\n",
    "        return np.ones(length) * split\n",
    "\n",
    "    n_splits = len(train_windows)\n",
    "    n_timepoints = len(y)\n",
    "    len_test = len(test_windows[0])\n",
    "\n",
    "    train_color, test_color = sns.color_palette(\"colorblind\")[:2]\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=plt.figaspect(0.3))\n",
    "\n",
    "    for i in range(n_splits):\n",
    "        train = train_windows[i]\n",
    "        test = test_windows[i]\n",
    "\n",
    "        ax.plot(\n",
    "            np.arange(n_timepoints), get_y(n_timepoints, i), marker=\"o\", c=\"lightgray\"\n",
    "        )\n",
    "        ax.plot(\n",
    "            train,\n",
    "            get_y(len(train), i),\n",
    "            marker=\"o\",\n",
    "            c=train_color,\n",
    "            label=\"Window\",\n",
    "        )\n",
    "        ax.plot(\n",
    "            test,\n",
    "            get_y(len_test, i),\n",
    "            marker=\"o\",\n",
    "            c=test_color,\n",
    "            label=\"Forecasting horizon\",\n",
    "        )\n",
    "    ax.invert_yaxis()\n",
    "    ax.yaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "    ax.set(\n",
    "        title=title,\n",
    "        ylabel=\"Window number\",\n",
    "        xlabel=\"Time\",\n",
    "        xticklabels=y.index,\n",
    "    )\n",
    "    # remove duplicate labels/handles\n",
    "    handles, labels = [(leg[:2]) for leg in ax.get_legend_handles_labels()]\n",
    "    ax.legend(handles, labels);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data\n",
    "\n",
    "We use a fraction of the Box-Jenkins univariate airline data set, which shows the number of international airline passengers per month from 1949 - 1960. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period\n",
       "1949-01    112.0\n",
       "1949-02    118.0\n",
       "1949-03    132.0\n",
       "1949-04    129.0\n",
       "1949-05    121.0\n",
       "Freq: M, Name: Number of airline passengers, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We are interested on a portion of the total data set.\n",
    "# (for visualisatiion purposes)\n",
    "y = load_airline().iloc[:30]\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plot_series(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing temporal cross-validation window splitters\n",
    "\n",
    "Now we describe each of the splitters.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A single train-test split using `temporal_train_test_split`\n",
    "\n",
    "This one splits the data into a traininig and test sets. You can either (i) set the size of the training or test set or (ii) use a forecasting horizon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# setting test set size\n",
    "y_train, y_test = temporal_train_test_split(y=y, test_size=0.25)\n",
    "fig, ax = plot_series(y_train, y_test, labels=[\"y_train\", \"y_test\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# using forecasting horizon\n",
    "fh = ForecastingHorizon([1, 2, 3, 4, 5])\n",
    "y_train, y_test = temporal_train_test_split(y, fh=fh)\n",
    "plot_series(y_train, y_test, labels=[\"y_train\", \"y_test\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Single split using `SingleWindowSplitter`\n",
    "\n",
    "This class splits the time series once into a training and test window. Note that this is very similar to `temporal_train_test_split`.\n",
    "\n",
    "Let us define the parameters of our fold:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set splitter parameters\n",
    "window_length = 5\n",
    "fh = ForecastingHorizon([1, 2, 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Folds = 1\n"
     ]
    }
   ],
   "source": [
    "cv = SingleWindowSplitter(window_length=window_length, fh=fh)\n",
    "n_splits = cv.get_n_splits(y)\n",
    "print(f\"Number of Folds = {n_splits}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us plot the unique fold generated. First we define some helper functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_windows(y, cv):\n",
    "    \"\"\"Generate windows\"\"\"\n",
    "    train_windows = []\n",
    "    test_windows = []\n",
    "    for i, (train, test) in enumerate(cv.split(y)):\n",
    "        train_windows.append(train)\n",
    "        test_windows.append(test)\n",
    "    return train_windows, test_windows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we generate the plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_windows, test_windows = get_windows(y, cv)\n",
    "plot_windows(y, train_windows, test_windows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([27, 28, 29])]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_windows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([22, 23, 24, 25, 26])]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_windows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sliding windows using `SlidingWindowSplitter`\n",
    "\n",
    "This splitter generates folds which move with time. The length of the training and test sets for each fold remains constant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Folds = 23\n"
     ]
    }
   ],
   "source": [
    "cv = SlidingWindowSplitter(window_length=window_length, fh=fh)\n",
    "\n",
    "n_splits = cv.get_n_splits(y)\n",
    "print(f\"Number of Folds = {n_splits}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_windows, test_windows = get_windows(y, cv)\n",
    "plot_windows(y, train_windows, test_windows)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sliding windows using `SlidingWindowSplitter` with an initial window\n",
    "\n",
    "This splitter generates folds which move with time. The length of the training and test sets for each fold remains constant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Folds = 18\n"
     ]
    }
   ],
   "source": [
    "cv = SlidingWindowSplitter(window_length=window_length, fh=fh, initial_window=10)\n",
    "\n",
    "n_splits = cv.get_n_splits(y)\n",
    "print(f\"Number of Folds = {n_splits}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_windows, test_windows = get_windows(y, cv)\n",
    "plot_windows(y, train_windows, test_windows)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Expanding windows using `ExpandingWindowSplitter`\n",
    "\n",
    "This splitter generates folds which move with time. The length of the training set each fold grows while test sets for each fold remains constant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Folds = 23\n"
     ]
    }
   ],
   "source": [
    "cv = ExpandingWindowSplitter(initial_window=window_length, fh=fh)\n",
    "\n",
    "n_splits = cv.get_n_splits(y)\n",
    "print(f\"Number of Folds = {n_splits}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_windows, test_windows = get_windows(y, cv)\n",
    "plot_windows(y, train_windows, test_windows)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multiple splits at specific cutoff values - `CutoffSplitter`\n",
    "\n",
    "With this splitter we can manually select the cutoff points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Folds = 4\n"
     ]
    }
   ],
   "source": [
    "# Specify cutoff points (by array index).\n",
    "cutoffs = np.array([10, 13, 15, 25])\n",
    "\n",
    "cv = CutoffSplitter(cutoffs=cutoffs, window_length=window_length, fh=fh)\n",
    "\n",
    "n_splits = cv.get_n_splits(y)\n",
    "print(f\"Number of Folds = {n_splits}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_windows, test_windows = get_windows(y, cv)\n",
    "plot_windows(y, train_windows, test_windows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([ 6,  7,  8,  9, 10]),\n",
       " array([ 9, 10, 11, 12, 13]),\n",
       " array([11, 12, 13, 14, 15]),\n",
       " array([21, 22, 23, 24, 25])]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_windows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([11, 12, 13]),\n",
       " array([14, 15, 16]),\n",
       " array([16, 17, 18]),\n",
       " array([26, 27, 28])]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_windows"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
